JP2022530963A - Inhaler system - Google Patents

Abstract

A system (10) is provided for calculating the probability of an asthma exacerbation in a subject. The system comprises a first inhaler (100) for administering rescue medication to a subject. Rescue drugs are suitable for treating impending respiratory ailments in a subject, for example, by rapidly dilating the bronchi or bronchioles upon inhalation of the drug. The first inhaler has a use detection system (12B) configured to count the number of rescue inhalations made by the subject using the first inhaler. The system optionally has a second inhaler for administering a maintenance medication to the subject during scheduled inhalation. The sensor system (12A) is configured to measure parameters related to airflow during emergency inhalation and/or during routine inhalation when a second inhaler is included in the system. The system further comprises a processor (14) configured to measure the number of emergency inhalations within the first period of time and receive parameters measured during at least some emergency inhalations and/or routine inhalations. . The processor uses the weighted model to calculate the probability of asthma exacerbation based on the number of rescue inhalations and parameters. The model is weighted so that the number of rescue inhalations is more significant than the parameters in the probability calculation. Further provided is a method for calculating the probability of an asthma exacerbation in a subject. The method uses a weighted model. [Selection drawing] Fig. 2

Description

The present invention relates to inhaler systems, in particular systems and methods for calculating the probability of exacerbations of respiratory disease.

Many respiratory disorders, such as asthma or chronic obstructive pulmonary disease (COPD), are lifelong, with treatment including lifelong administration of drugs to manage the patient's symptoms and reduce the risk of irreversible lesions. I'm sick. Currently, there is no cure for diseases like asthma and COPD.
Treatment takes two forms. First, in terms of maintenance treatment, it is intended to reduce airway inflammation and, as a result, control symptoms in the future. Maintenance treatment is typically given by prescribing inhaled corticosteroids alone or in combination with long-acting bronchodilators and / or muscarinic antagonists. Second, the treatment also has an aspect of emergency (or rescue) treatment, in which the patient is administered a fast-acting bronchial dilator to relieve severe attacks of asthma, cough, chest compressions and dyspnea. To.
Patients with respiratory illnesses such as asthma or COPD may also experience a temporary relapse or exacerbation that exacerbates the symptoms of the respiratory illness. In the worst case, exacerbations can be life-threatening for the patient.

If an exacerbation of an imminent respiratory illness can be identified, the action plan can be improved and, for example, unscheduled visits to or from a doctor, hospitalization and systemic administration of steroids before the patient's symptoms require. It will be possible to provide opportunities for such advanced treatment.

Therefore, there is a need for improved methods to identify the risk of exacerbation of imminent respiratory illness in the prior art.

Therefore, the present invention is a system for calculating the probability of exacerbation of asthma in a subject, comprising a first inhaler for administering an emergency drug to the subject, and the first inhaler is the subject. It has a use detection system configured to measure the emergency inhalation performed by using one inhaler and is also selective for administering maintenance medication to the subject during routine inhalation. A second inhaler and, when the second inhaler is used in the system, was configured to measure airflow parameters during emergency inhaler and / or regular inhaler using the second inhaler. Using a sensor system and a weighted model that measures the number of emergency inhalations during the first period, receives parameter measurements during at least several emergency and / or regular inhalations, and uses an emergency inhaler Equipped with a processor configured to calculate the probability of asthma exacerbation based on the number of times and parameter measurements, the model allows the number of emergency inhalations to be more significant than the parameter measurements in the probability calculation. It provides a system characterized by being weighted.

A model for predicting exacerbation of asthma by using the number of emergency inhalations and measurements of airflow parameters during emergency and / or regular inhalations, eg, ignoring any of these factors. It is possible to provide a model that can be predicted with higher accuracy than the model.
Furthermore, when predicting the exacerbation of asthma, the number of emergency inhalations was made more significant than the airflow parameters in the probability calculation. As a result, the model is weighted so that the number of emergency inhalations is more significant than the inhalation parameters in the probability calculation, which improves the accuracy of the probability calculation. This is in contrast to the trend of predicting exacerbations of COPD, where inhalation parameters were more significant than the number of emergency inhalations in predicting exacerbations.

It is a block diagram of the system according to 1 Example of this invention. It is a figure which shows the system by another embodiment of this invention. It is a flow chart of the method by 1 Example of this invention. It is a flow chart and a timeline of the method by another Example of this invention. It is a figure which shows the timeline of the inhalation of an emergency medicine. It is a graph of the average number of emergency inhalations vs. the number of days since the exacerbation of asthma. It is another graph of the average number of emergency inhalations vs. the number of days since the exacerbation of asthma. 4 are four graphs of the rate of change vs. the number of days since exacerbation of asthma with respect to each reference value for the number of emergency inhalations and various parameters for airflow. It is a figure which shows the receiver operating characteristic (ROC) curve analysis of the model for calculating the probability of exacerbation of asthma. It is a graph of the average number of emergency inhalations vs. the number of days since the exacerbation of COPD. It is another graph of the average number of emergency inhalations vs. the number of days since the exacerbation of COPD. It is a graph of the number of days from the exacerbation of the average maximum inhalation flow rate (L / min) vs. COPD. It is another graph of the number of days from the exacerbation of the average maximum inhalation flow rate (L / min) vs. COPD. It is a graph of the number of days from the exacerbation of the average inhalation amount (L) vs. COPD. Another graph of the number of days since the exacerbation of mean inhalation volume (L) vs. COPD. It is a graph of the average inhalation duration (s) vs. the number of days since the exacerbation of COPD. It is another graph of the average inhalation duration (s) vs. the number of days since the exacerbation of COPD. It is a figure which shows the receiver operating characteristic (ROC) curve analysis of the model for calculating the probability of the exacerbation of an imminent COPD. It is a perspective view of an inhaler. It is a vertical sectional view of the inhaler shown in FIG. It is an exploded perspective view of the inhaler shown in FIG. FIG. 18 is an exploded perspective view of the upper cap of the inhaler and the electronic module shown in FIG. It is a graph of the air flow rate vs. pressure of the inhaler shown in FIG.

It should be understood that the detailed description and examples provided exemplifying the devices, systems and methods are solely for the purpose of explaining the present invention and do not limit the scope of the present invention. be. Those and other features, aspects and advantages of the devices, systems and methods of the invention will be better understood from the following description of the specification, the appended claims and drawings. It should be understood that the drawings are only schematic and are not drawn to the actual dimensions. It should also be understood that the same or similar components are numbered the same throughout the drawings.

Asthma and COPD are chronic inflammatory diseases of the airways. They are both variable and characterized by recurrent symptoms of airway obstruction and bronchial paralysis. Symptoms include wheezing, coughing, chest compressions and dyspnea attacks.

Symptoms are controlled by avoiding triggers and using medications, especially inhaled medications. Drugs include inhaled corticosteroids and inhaled bronchodilators.

Inhaled corticosteroids (ICS) are steroid hormones used for long-term control of respiratory illness. They work by reducing airway inflammation. Inhaled corticosteroids include, for example, budesonide, beclomethasone (beclomethasone dipropionate), fluticasone (fluticasone propionate), mometasone (mometasone furancarboxylic acid ester), ciclesonide and dexametasone (sodium dexametasone). Parentheses represent the preferred salt or ester form.

Bronchodilators of different classifications target different receptors in the airways. The two commonly used categories are β ₂ stimulants and anticholinergic drugs.

β ₂ adrenergic stimulants (β ₂ stimulants) act on adrenergic receptors that induce smooth muscle relaxation and dilate the airways. Long-acting β ₂ stimulants (LABA) include, for example, formosterol (formotelol fumarate), salmesterol (salmeterol xinafoate), indacaterol (indacaterol maleate), vanbuterol (banbuterol hydrochloride), It contains clinbuterol (clinbuterol hydrochloride), odoratorol (orodaterol hydrochloride), carmoterol (carmoterol hydrochloride), turobterol (turobterol hydrochloride) and viranterol (viranterol triphenylacetate). Short-acting β ₂ stimulants (SABAs) include, for example, albuterol (albuterol sulfate).

Typically, short-acting bronchodilators provide rapid relief from acute bronchoconstriction (often referred to as "emergency medications" or "relievers"), while long-acting bronchodilators. Supports long-term symptom control and prevention.
However, some fast-acting, long-acting bronchodilators, such as formoterol (formoterol fumarate), can be used as first aid agents. That is, first aid provides relief from acute bronchoconstriction. First aid is taken as needed. The first aid may also take the form of a combination product such as, for example, ICS-formoterol (formoterol fumarate), typically budesonide formoterol fumarate hydrate inhaler. Thus, the emergency drug preferably consists of SABA or fast-expressing LABA, more preferably albuterol (albuterol sulfate) or formoterol (formoterol fumarate), and even more preferably albuterol (albuterol sulfate).

Albuterol (also known as salbutamol) is typically administered as a sulfate and is the preferred first aid agent in the present invention.

Anticholinergic agents (antimuscarinic agents) block the neurotransmitter acetylcholine by selectively blocking its receptors in nerve cells. Upon topical application, anticholinergic agents act primarily on M3 muscarinic donors located in the airways _, resulting in smooth muscle relaxation and bronchodilation.
Long-acting muscarinic antagonists (LAMA) include, for example, thiotropium (thiotropium bromide), oxytropium (oxytropium bromide), acridinium (acridinium bromide), ipratopium (ipratopium bromide), glycopyrronium (glycopyrro bromide). Nium), oxybutinine (oxybutinine hydrochloride or oxybutinine bromide hydrochloride), tortellodin (torterodin tartrate), trospium (trospium chloride), solifenasin (soliphenacin succinate), fesoterodin (fesoterodin fumarate) and dalifenasin. Contains (dalifenacin hydrobromide).

To date, numerous approaches have been taken in preparing and prescribing these agents for administration by inhalation using a dry powder inhaler (DPI), pressure metered inhaler (pMDI), nebulizer, etc. ..

According to the GINA (Global Initiative for Asthma) guidelines, a step-by-step approach is taken for the treatment of asthma.
In the first stage, which represents mild asthma, the patient is given SABA as needed, such as albuterol sulfate. Patients may also be given low doses of ICS-formoterol or low doses of ICS as needed whenever SABA is prescribed. In the second step, the usual low dose ICS is given with SABA or optionally low dose ICS-formoterol. In the third stage, LABA is added. In the fourth stage, the dose of each drug is increased, and in the fifth stage, additional treatment such as an anticholinergic agent or a low dose of oral corticosteroid is included. That is, each stage can be regarded as a treatment plan, and each treatment is structured according to the degree of acute severity of respiratory disease.

COPD is the leading cause of death worldwide. COPD is a heterogeneous, long-term illness that includes chronic bronchitis, emphysema, and even the narrow airways. Lesions in patients with COPD are primarily confined to the airways, pulmonary parenchyma and pulmonary vasculature. These lesions reduce the healthy ability of the lungs to inhale and exhale gas.

Bronchitis is characterized by long-term inflammation of the trachea. Common symptoms include wheezing, dyspnea, coughing and sputum production, all of which are bothersome and detrimental to the patient's quality of life. Emphysema is also associated with long-term bronchial inflammation, in which case the response to inflammation results in destruction of lung tissue and progressive airway narrowing. Over time, lung tissue loses its natural elasticity and expands. When this happens, the gas exchange function is impaired and the breathed gas is often trapped in the lungs. This causes localized hypoxia and reduces the amount of oxygen delivered to the patient's bloodstream per inspiration. And the patient experiences dyspnea.

Patients living with COPD experience these various symptoms on a daily basis, although some may be part of them. Disease severity is determined by a variety of factors, but most commonly correlates with disease progression. Their symptoms represent stable COPD, regardless of the severity of the disease, and this condition is maintained and controlled through the administration of various agents. Treatment may vary, but often includes inhaled bronchodilators, anticholinergic agents, long-acting and short-acting β ₂ stimulants and inhaled corticosteroids. The drug is often given as a single treatment or as a combination treatment.

Patients are categorized by the severity of their COPD using the categories defined in the GOLD (Global Initiative for Chronic Obstructive Lung Disease, Inc .) guidelines. The categories consist of A to D, and the first recommended treatment options vary from category to category. Patient group A is prescribed a short-acting muscarinic antagonist (SAMA) or a short-acting β ₂ stimulant (SABA) as needed. Patient group B is prescribed a long-acting muscarinic antagonist (LAMA) or a long-acting β ₂ stimulant (LABA). Patient group C is prescribed inhaled corticosteroid (ICS) + LABA, or LAMA. Patient group D is prescribed ICS + LABA and / or LAMA.

Patients with respiratory illnesses such as asthma or COPD suffer from periodic exacerbations that go beyond the scope of daily basic changes in the condition. Exacerbations are acute exacerbations that require additional treatment, that is, treatment that goes beyond maintenance treatment.

For asthma, additional treatment for moderate exacerbations consists of repeated doses of SABA and oral corticosteroids and / or controlled oxygen inhalation (the latter requiring hospitalization). For significant exacerbations, anticholinergic agents (typically iplatopium bromide), nebulized SABA or magnesium sulphate are added.

For COPD, additional treatment for moderate exacerbations consists of repeated doses of SABA, oral corticosteroids and / or antibiotics. Controlled oxygen inhalation and / or respiratory support (both requiring hospitalization) are added for significant exacerbations.

As used herein, the term "exacerbation" includes both moderate and severe exacerbations.

The present invention relates to a therapeutic approach that can intervene early in the treatment of a patient by predicting the exacerbation of respiratory disease, thereby improving the outcome of the patient.

INDUSTRIAL APPLICABILITY According to the present invention, a system for calculating the probability of exacerbation of asthma in a subject is provided. The system is equipped with a first inhaler for administering the first aid to the subject. First aid is suitable for treating exacerbations of respiratory symptoms, for example by rapidly dilating the bronchi and bronchioles when inhaling the drug. The first inhaler has a use detection system configured to measure the emergency inhalation performed by the subject using the first inhaler. The system optionally has a second inhaler for administering the maintenance agent to the subject during regular inhalation. The sensor system is configured to measure parameters with respect to airflow during emergency inhalation and / or periodic inhalation using the second inhaler when the second inhaler is used in the system.

The first aid is as described above and typically consists of SABA, or LABA with rapid expression such as formoterol (formoterol fumarate). The first aid also has the form of a combination product such as, for example, ICS-formoterol (formoterol fumarate), typically budesonide formoterol (budesonide formoterol phthalate).
Such an approach is called "MART" (maintenance and rescue therapy). However, the presence of an emergency drug indicates that it is the first inhaler herein. Because the presence of first aid is decisive in the weighted model. Therefore, it covers both emergency medicines and combinations of emergency medicines and maintenance medicines. In contrast, the second inhaler, when used, is only used in terms of maintenance and management of treatment, not for emergency purposes. An important difference is that the first inhaler is used as needed, while the second inhaler is used on a regular basis at a given time.

The system further comprises a processor configured to measure the number of emergency inhalations during the first period and receive parameter measurements during at least several emergency and / or regular inhalations. The processor uses a weighted model to calculate the probability of exacerbation of asthma based on the number of emergency inhalations and parameter measurements. The model is weighted so that the number of emergency inhalations is more significant than the parameter measurements in the probability calculation.
The present invention further provides a method of calculating the probability of exacerbation of asthma in a subject. The method uses a weighted model. Any of the preferred embodiments described with respect to the system of the invention can be applied to this method and vice versa.

Attempts have been made to assess the risk of exacerbations of imminent respiratory illness, such as asthma or exacerbation of COPD, by monitoring factors and environmental factors associated with a variety of subjects. Challenges have been made on which factors should be taken into account and which should be ignored. By ignoring factors that have a negligible or negligible impact on risk assessment, risk is more efficient, for example, with less processing resources, resources for calculations such as battery power and memory capacity, etc. It becomes possible to calculate. More importantly, it is necessary to improve the accuracy of calculating the probability of exacerbation of imminent respiratory illness. A more accurate risk assessment can result in a more effective alarm system, resulting in better medical intervention for the subject. That is, a more accurate assessment of the risk of exacerbations has the potential to guide intervention in the subject in the event of an urgent risk.

If the probability of exacerbation is high, a gradual change in treatment plan may be justified, for example, for a treatment that was configured for the subject at the time of urgent risk. Alternatively, if the probability of exacerbations is low over the long term, more accurate probability calculations can be used as a guide to justify the downgrade or even withdrawal of the current treatment. This means, for example, that subjects are no longer required to take high doses of medication that are no longer commensurate with the pathology of their respiratory illness.

The present inventor has conducted extensive clinical studies and, as described in detail below, a first-aid drug emergency calculation performed by a subject within a period of time (first period) to calculate the probability of exacerbation of asthma disease. Accuracy in calculating the probability of exacerbation of asthma by using a weighted model that is based on both the number of inhalations and the parameters (measurements) of airflow during the inhalation of emergency and / or maintenance medications. I found that I could raise it.

The first period corresponds to a sampling period in which the number of emergency inhalations is measured. The first period is, for example, 1 to 15 days. This sampling period is selected so that the period is of appropriate length for data collection of the number of emergency inhalations. If the sampling period is too short, we will not be able to collect the amount of data needed for a reliable prediction of exacerbations, and if the sampling period is too long, we will distinguish it from short-term trends that are meaningful for diagnosis or prediction. An averaging effect that makes it difficult to attach will occur.

By using both the number of emergency inhalations and the parameters (values), it is possible to form a model that allows for more accurate predictions than, for example, a model that ignores one of these factors. In addition, clinical studies have found that trends in the number of emergency inhalations and the use of emergency inhalers are more significant in calculating the probability of exacerbation of asthma than parameters for airflow during inhalation. The parameters are still a significant factor in calculating the probability of exacerbation of asthma, but have a small effect on the probability when compared to the number of emergency inhalations.
Therefore, by weighting the model so that the number of emergency inhalations is more significant than the parameter in the probability calculation, the accuracy of the probability calculation is further improved.

The model has, for example, a first weighting factor associated with the number of emergency inhalations and a second weighting factor associated with the parameter. The first weighting factor is greater than the second weighting factor when standardized to evaluate the different units used to quantify the number of first-aid inhalations (or trends associated with first-aid drug use) and parameters. It increases, which ensures that the number of emergency inhalations is more significant than the parameter in the probability calculation.

Probability calculations are based in part on the number of emergency inhalations. Placing the basis of the probability calculation on the number of emergency inhalations means that the model uses one or more trends based on the absolute number of emergency inhalations and / or the number of emergency inhalations during the first period. .. This tendency consists of fluctuations in the number of emergency inhalations, not the number of emergency inhalations themselves.

Trends based on the number of emergency inhalations include, for example, the number of inhalations made during a particular time of the day. Therefore, for example, the number of inhalations at night is included in the number of inhalations as one factor. The processor, for example, has a suitable clock function to record the use of emergency medicine during such time of the day.

The first weighting factor weights a tendency of 1 or 2 or more based on the absolute number of emergency inhalations and / or the number of emergency inhalations.

More generally, the number of emergency inhalations (including, for example, any relevant trends) is 40% to 95%, preferably 55% to 95%, more preferably (relative to other factors) in the model. It has a significance / importance (weight) of 60% to 85%, most preferably 60% to 80%, for example about 60%.

The probabilistic calculation is also based on parameters for airflow during emergency inhalation and / or regular inhalation using the second inhaler when provided with a second inhaler. The parameters correspond to a single factor for airflow during inhalation, or include multiple such factors. For example, the parameter is at least one of maximum inhalation flow rate, inhalation volume, inhalation duration and inhalation rate. The time vs. maximum inhalation flow rate provides, for example, a measure of inhalation rate.

Putting the basis of the probability calculation on the parameters means that the model uses one or more factors with respect to the airflow during inhalation and / or one or more tendencies associated with each factor. Such a tendency corresponds to the fluctuation of each factor.

The second weighting factor weights one or more factors with respect to airflow during inhalation and / or one or more tendencies associated with each factor.

More generally, the inhalation parameters (including, for example, any relevant tendencies) are 2% to 49% or 2% to 30%, preferably 2% to 45%, more preferably 5% to 40% in the model. It has a percentage, most preferably 10% to 35%, eg, about 10% or about 35% significance / significance (eg, weight).

The probability of asthma exacerbation is the probability of an imminent asthma exacerbation that occurs during the exacerbation period following the first period. In this way, the model can calculate inhalation data, the number of emergency inhalations, and the probability of exacerbation of asthma occurring within a predetermined period called the "exacerbation period" following the first period in which parameter data is collected. To. The exacerbation period is, for example, 1 to 10 days, for example, 5 days. The exacerbation period is selected based on the ability of the model to predict exacerbations within that period, ensuring that the predetermined period is long enough to take appropriate treatment steps, if necessary.

In some embodiments, biological parameters are included in the model to improve the accuracy of the weighted model. In such an embodiment, the processor is configured to receive, for example, parameters (parameter values) relating to the living body. A data entry unit is included, for example, in the system to allow subjects and / or healthcare professionals to enter parameters related to the living body.

The model is, for example, weighted so that parameters relating to the living body have a lower significance than the number of emergency inhalations in the probability calculation. In other words, the third weighting factor is set to be related to the parameters relating to the living body, and the third weighting factor is set to be smaller than the first weighting factor related to the number of emergency inhalations. The third weighting factor is set to be greater or lesser than the second weighting factor associated with the airflow parameter.

Preferably, the third weighting factor is smaller than the second weighting factor. In order of predictive power, the use of first aid drugs has the greatest impact, followed by inhalation parameters, followed by biological parameters.

Biological parameters include, for example, body weight, height, body mass index, blood pressure including systolic and / or diastolic blood pressure, gender, race, age, smoking history, sleep / activity pattern, exacerbation history, and subject. One or more parameters selected from another treatment given, another drug administered, and so on. In one embodiment, the biological parameters include age, body mass index and exacerbation history. In another embodiment, biological parameters include exacerbation history and medical history, body mass index and blood pressure, such as systolic and / or diastolic blood pressure.

More generally, the biological parameters are 1% -15%, preferably 1% -12%, more preferably 3% -10%, most preferably 4% -10%, eg about 5%, in the model. Or have about 8% significance / significance (eg weight).

In a non-limiting example, the number of emergency inhalations (eg, including relevant tendencies) is 40% to 95%, preferably 55% to 90%, more preferably 60% to 85%, most preferably in the model. It has a significance / significance (eg, weight) of 60% to 80%, such as about 60% or about 80%, and inhalation parameters (eg, including relevant tendencies) are preferably 2% to 49% in the model. Has a significance / significance (eg, weight) of 2% to 45%, more preferably 5% to 40%, most preferably 10% to 35%, such as about 10% or about 35%, and parameters relating to the living body. Is 1% to 15%, preferably 1% to 12%, more preferably 3% to 10%, most preferably 4% to 10%, eg, about 5% or about 8% significance / significance in the model. Has (eg weight).

More generally, additional data sources such as environmental data regarding the level of weather or pollution may be added to the model. Such additional data is weighted to have a lower significance than the data on the number of emergency inhalations and, optionally, less than the data on the inhalation parameters, with respect to the probability calculation.

The number of maintenance / regular inhalations, selectively or additionally, provides useful information for predicting exacerbations. This is because a smaller number of maintenance / regular inhalations (indicating poor adherence to maintenance medications) increases the risk of exacerbations.

In a relatively simple example, an increase in the number of emergency inhalations using the first inhaler (relative to the reference period for the subject in question) and / or a decrease in the number of regular inhalations using the second inhaler (relative to the reference period for the subject in question). Poor adherence to the treatment regimen), along with inhalation parameters, represents a deterioration in lung function that increases the probability of exacerbation of lung disease.

In a specific example, adherence to maintenance medications decreased from 80% to 55%, emergency inhaler use increased by 67.5%, maximum inhalation flow decreased by 34%, and inhalation volume decreased by 23%. (They are all rates of change from the patient's reference value), and when two exacerbations occurred the previous year and the BMI exceeded 28, using ROC-AUC (FIGS. 8 and 17 below). The probability of exacerbation of asthma over the next 5 days is 0.87.

More generally, the number of emergency inhalations (including, for example, trends with respect to the number of emergency inhalations) is the most significant factor in the probability calculation.

The model is a linear or non-linear model. The model consists of, for example, a machine learning model. For example, a supervised model such as a supervised machine learning model is used. Regardless of the specific form of the model used, as already described, the model is configured to be more sensitive, i.e., responsive to the number of emergency inhalations than the inhalation parameters. This sensitivity corresponds to the "weighting" of the weighted model.

In a non-limiting example, the model is configured to use a decision tree. Other suitable techniques such as neural networks or deep learning models may also be considered by those of skill in the art.

Regardless of predicting the exacerbation of respiratory illness, the system's processors calculate the probability of exacerbation based on the number of inhalations, inhalation parameters, and indicators of the condition of the respiratory illness suffering by the subject. By including the index in the prediction, the prediction accuracy is improved. The reason is that the user-entered metric proves the validity of the predicted value of the probability assessment derived by taking into account the number of inhalations and the inhalation parameters, for example, without using the user-entered metric, and is more certain. Because it helps to make things.

In one embodiment, the processor calculates the initial probability of exacerbation of respiratory disease based on the number of inhalations recorded and the inhalation parameters (values) received, but not on the index. The initial probabilities are calculated using, for example, a weighted model as described above. Probability, or overall probability, is then calculated based on the number of inhalations, inhalation parameters, and the received index of the symptoms of the respiratory illness suffered by the subject. For example, the overall probability is calculated based on the initial probability and the received index.

The initial probability determines, for example, the risk of exacerbations for the next 10 days. The overall probability of taking into account the indicators of the condition of the respiratory illness suffered by the subject determines, for example, the risk of exacerbations over the next 5 days. That is, by including the index in the probability calculation, more accurate prediction in a shorter period becomes possible.

By including user-entered indicators in the probability calculation, positive and negative predictions, prediction sensitivity, that is, the ability of the system / method to correctly determine that the subject is at risk (true positive rate), and Prediction specificity, i.e. one or more of the system / method's ability to correctly determine that a subject is not at risk (true negative rate), is enhanced.

Data on the number of inhalations and inhalation parameters represent, for example, deviations from the subject's reference values for 10 days prior to exacerbations. Including user-entered indicators in subsequent predictions improves positive and negative predictions, as well as the sensitivity and specificity of the prediction system / method.

The processor is configured, for example, to output a prompt to the user and control the user interface to receive input from the user. Prompts are output based on the number of inhalations and inhalation parameters, but not on the basis of indicators, but on the calculated initial probabilities. The prompt is output, for example, based on the initial probability of reaching or exceeding a predetermined threshold. In this way, the user is prompted by the system to enter an indicator based on the initial probability of an imminent exacerbation. By inputting the metric, the (overall) probability that takes the metric into account helps to support and justify the initial probability.

This is considered, for example, the "analysis-driven use" of the indicator. User input is required when data on the number of inhalations and inhalation parameters indicate a possible exacerbation of the subject's respiratory illness.

The user interface is prompted, for example, to enter the metric by completing a short questionnaire through a pop-up notification to the user or target audience. The logic that determines when to issue a pop-up notification is triggered by a shift in key variables such as the number and / or duration of emergency and / or maintenance inhalations, and changes in inhalation parameters.

Optionally or additionally, the system is configured to receive the metric when the user chooses to enter the metric through the user interface. For example, when a healthcare professional determines that an indicator is useful in ensuring the calculation of initial probabilities. This is considered, for example, the "on demand" use of the indicator. This request is made, for example, by the patient or the physician in charge of the patient prior to or during the evaluation by a medical professional.

Thus, the user is only prompted to enter an indicator when this is deemed necessary by the system and / or healthcare professional. This advantageously reduces the burden on the subject and inputs indicators when the subject is prompted or prompted for input, i.e., when such input is desirable for monitoring the subject's respiratory illness. Make it possible. Entering the indicators in these examples is more feasible than when the subject is routinely prompted to enter the indicators.

In one embodiment, the user interface is configured to provide multiple user selectable respiratory disease pathologies. In this case, the index is determined by the user's choice of at least one medical condition option.

For example, the user interface displays a questionnaire consisting of multiple questions, each answer corresponding to an indicator. A user, such as the subject or the healthcare professional responsible for the subject, uses the user interface to enter the answer to the question.

The questionnaire is relatively short, that is, it consists of a relatively small number of questions in order to minimize the burden on the subject. Nevertheless, the number and content of questions are set by the indicator to ensure that the calculation of the probability of exacerbation is more reliable than if no indicator was entered.

More generally, the purpose of the questionnaire is to provide an immediate or relatively quick understanding of the subject's living conditions (related to respiratory illness) with a small number of timely questions answered relatively quickly. The purpose is to identify recent indicators (eg, within the last 24 hours). The questionnaire will be translated into the subject's local language.

General control questionnaires, especially ACQ / T (Asthma Control Questionnaire / Test) for asthma, or CAT (COPD Assessment Test) for COPD, recall the patient's past medical conditions. Focusing on the past rather than the present can adversely affect the value of the purpose of predictive analysis.

Next, a non-limiting example of such a questionnaire will be described. For each question, the subject chooses one of the situational options, always (5), usually (4), sometimes (3), rare (2), and none (1).
1. 1. How often do you experience dyspnea, or how often do you develop dyspnea?
2. 2. How often do you experience a cough, or how often do you develop a cough?
3. 3. How often do you experience wheezing, and what is the rate of wheezing?
4. How often do you experience chest compressions, or how often do you develop chest compressions?
5. How often do you experience nighttime onset / sleep effects, or what percentage of your nighttime onset / sleep effects?
6. How often do you experience work, school or home restrictions, or what percentage of your work, school or home limits?

Another example of a questionnaire looks like this:
1. 1. Do you experience more respiratory symptoms than usual (yes / no)?
If you answered "yes":
2. 2. Are you experiencing more chest compressions or dyspnea (yes / no)?
3. 3. Are you experiencing more coughs (yes / no)?
4. Are you experiencing more wheezing (yes / no)?
5. Does it affect your sleep (yes / no)?
6. Does it limit your home / work / school activities (yes / no)?

Answers to these questions are used, for example, to calculate scores, which are included in or correspond to indicators of the pathology of the respiratory illness suffered by the subject.

In one embodiment, the user interface is configured to provide contextual choices in the form of selectable icons, such as pictogram icons and / or checkboxes and / or sliders and / or dials. Thus, the user interface provides a direct and intuitive way to enter indicators of the pathology of the respiratory illness suffered by the patient. Such an intuitive input method is particularly advantageous when the subject himself / herself inputs an index related to himself / herself. This is because the relatively easy user input is hardly hindered by the worsening of the subject's respiratory illness.

Appropriate user interfaces are used for the purpose of allowing user input of indicators of the pathology of the respiratory illness suffered by the subject. For example, the user interface comprises or consists of a (first) user interface for a user-owned device. User-owned devices are, for example, personal computers, tablet computers and / or smartphones. When the user-owned device is a smartphone, the (first) user interface corresponds, for example, to the touch screen of the smartphone.

In one embodiment, the processor of the system includes at least a (first) processor in a user-owned device. Optionally or additionally, the first inhaler and / or the second inhaler has, for example, a (second) processor, the processor of the system is at least partially included in the inhaler ( A second processor) is included.

The method of the present invention is provided to calculate the probability of exacerbation of asthma in a subject. The method measures the number of emergency inhalations of an emergency drug suitable for the treatment of the subject's imminent respiratory disease performed by the subject during the first period, during and / or at least several emergency inhalations. Alternatively, measure parameters for airflow during routine inhalation of maintenance medications performed by the subject, and use a weighted model to calculate the number of emergency inhalations and the probability of exacerbation of asthma based on the measured values of the parameters. The model is characterized in that the number of emergency inhalations is weighted to be more significant than the measured value of the parameter in the calculation of probability.

Further, according to the present invention, a method for treating an exacerbation of asthma in a subject, wherein the method for calculating the probability of exacerbation of asthma described above is carried out, and the probability reaches a predetermined upper limit or is set to an upper limit. Determine if it exceeds, or determine if the probability reaches or is below a predetermined lower limit, and based on the probability of reaching or exceeding the upper limit, or reaching the lower limit A method comprising treating asthma based on the probability of being below the lower limit is provided.

Treatment involves using an inhaler to administer an emergency drug to the subject when the probability reaches or exceeds a predetermined upper limit.

This treatment modifies existing treatments for asthma. The existing treatment has a first treatment plan, and the modification of the existing treatment is based on the probability that a predetermined threshold has been reached or exceeded from the first treatment plan. Composed of changing to a treatment plan, a second treatment plan is created for exacerbations of asthma that are at higher risk than the first treatment plan.

More accurate risk calculations using weighted models facilitate the realization of more effective warning systems, resulting in appropriate medical intervention for the subject. That is, a more accurate assessment of the risk of exacerbations can guide intervention in the subject at critical risk. In particular, the intervention involves carrying out a second treatment plan. This includes, for example, advancing the subject to a higher stage as specified in the GINA or GOLD guidelines. Such preemptive interventions do not require the subject to continue to suffer from exacerbations or to continue to be exposed to the risks involved in order to proceed to a second treatment plan that should be justified. means.

In one example, the second treatment plan consists of administering a biopharmacy to the subject. Relatively expensive biopharmacy means that stepping up a subject's treatment to include biopharmacy administration tends to require careful consideration and justification.
The systems and methods according to the invention can justify the administration of biopharmacy by providing a reliable prediction of the subject's risk of suffering exacerbations. For example, when the calculated probability reaches or exceeds a predetermined minimum number of times, an upper limit representing a high-risk exacerbation, the administration of the biopharmacy is quantitatively justified and the biopharmacy is administered accordingly. Will be done.

More generally, biopharmacy consists of any one of omalizumab, mepolizumab, reslizumab, benralizumab and dupilumab, or a combination of two or more thereof.

Changing existing treatments for asthma is based on the probability that a predetermined lower limit will be reached or lower, from the first treatment plan to a lower risk exacerbation of asthma than the first treatment plan. It consists of changing to the created third treatment plan.

For example, if the probability of exacerbations is low over a relatively long period of time, reliable accurate probability calculations can be used as advice to justify downgrading or even excluding existing treatment regimens. In particular, subjects are transitioned from a first treatment plan to a third treatment plan created for exacerbations of lower-risk respiratory illness than the first treatment plan. This includes, for example, advancing the subject to a lower stage as specified in the GINA or GOLD guidelines.

Further, according to the present invention, a method for diagnosing an exacerbation of asthma, wherein the method for calculating the probability of exacerbation of asthma in the above-mentioned subject is carried out, and the probability is a predetermined upper limit representing the exacerbation of asthma. Provided is a method comprising determining whether to reach or exceed the upper limit and diagnosing an exacerbation of asthma based on the probability of reaching or exceeding the upper limit.

Further, according to the present invention, a method for diagnosing the severity of asthma in a subject and calculating the probability of exacerbation of asthma in the subject described above is carried out, and the probability is that asthma is more severe. Determine if a predetermined upper limit has been reached or exceeded, or if the probability has reached or is lower than the lower limit, which indicates that asthma is milder. Determine and diagnose asthma as more severe based on the probability of reaching or exceeding the upper limit, or more mild asthma based on the probability of reaching or lowering the lower limit A method characterized by that is provided.

Further, according to the present invention, there is a method for defining the boundaries of the subpopulations of the subjects, and the above-mentioned method is carried out for each subject belonging to the population of the subjects, thereby performing the population. Calculate the probability of exacerbation of asthma for each subject belonging to, set the threshold or range of probabilities to distinguish the calculated probabilities for the subpopulation from the calculated probabilities for the rest of the population, and set the probabilistic A method is provided characterized in that a subpopulation is defined from the rest of the population using a range of thresholds or probabilities.

FIG. 1 is a block diagram of a system 10 according to an embodiment of the present invention. The system 10 includes a first inhaler 100 and a processor 14. The first inhaler 100 is used to administer an emergency drug such as SABA to the subject. SABA contains, for example, albuterol. The first inhaler 100 has a sensor system 12A and / or a use detection system 12B.

The system 10 is selectively referred to, for example, as an "inhaler assembly".

The first inhaler is selectively referred to, for example, as an "emergency inhaler".

The second inhaler is selectively referred to, for example, as a "maintenance inhaler" or a "managed inhaler".

The number of emergency inhalations is measured by the use detection system 12B included in the first inhaler 100.

The sensor system 12A is configured to measure parameters. The sensor system 12A has one or more sensors such as, for example, one or more pressure sensors and / or temperature sensors and / or humidity sensors and / or direction sensors and / or acoustic and / or optical sensors. ing. Pressure sensors include barometric pressure sensors (eg, atmospheric pressure sensors), differential pressure sensors, absolute pressure sensors, and the like. The sensor uses the technology of microelectromechanical systems (MEMS) and / or nanoelectromechanical systems (NEMS).

Pressure sensors are particularly suitable for measuring parameters. This is because it is monitored by measuring the pressure changes associated with the airflow during inhalation by the subject. As described in more detail below with reference to FIGS. 18-22, the pressure sensor may be, for example, in a flow path through which air and agents are inhaled by the subject during inhalation, or this flow. It is placed in communication with the road. Other methods of measuring parameters, such as using a flow sensor, will also be apparent to those of skill in the art.

Optionally or additionally, the sensor system 12A comprises a differential pressure sensor. The differential pressure sensor consists of, for example, a dual port type sensor for measuring the pressure difference across a part of the air flow path that the subject sucks through. Alternatively, a single port gauge type sensor is used. The latter measures the pressure difference in the air flow path between suction and the absence of airflow. Differences in measurements correspond to pressure drops associated with inhalation.

Although not shown in FIG. 1, the system 10 is further equipped with a second inhaler for administering maintenance agents to the subject during regular inhalation. The second inhaler has a sensor system 12A and / or a use detection system 12B that is different from the sensor system 12A and / or the use detection system 12B of the first inhaler 100. The sensor system 12A of the second inhaler is configured to measure parameters during regular inhalation. For example, the sensor system 12A has another pressure sensor, such as another microelectromechanical system pressure sensor or another nano electromechanical system pressure sensor for measuring parameters during inhalation of maintenance agents.

Thus, inhalation of one or both emergency and maintenance medications is used to collect information on the subject's lung function and / or lung health. When the first and second inhalers are used, the predictive accuracy of imminent exacerbations is improved by additional inhalation data provided by monitoring both regular and emergency inhalations.

Regular inhalation and emergency inhalation each reduce the pressure in the air flow path compared to when inhalation is not performed. The point where the pressure is at its lowest corresponds to the maximum suction flow rate. The sensor system 12A detects this point during inhalation. The maximum inhalation flow rate varies from inhalation to inhalation and depends on the subject's medical condition. The lower maximum inspiratory flow rate is recorded, for example, when the subject is approaching exacerbation. As used herein, the term "minimum maximum inhalation flow rate" is the maximum inhalation flow rate recorded for inhalations performed using the first and / or second inhaler within a given (second) period. It means the smallest of them.

The pressure changes associated with each inhalation are used to selectively or additionally measure the inhalation volume. This is achieved, for example, by using the pressure change measured by the sensor system 12A during inhalation to measure the flow rate during inhalation and then deriving the total inhalation volume. Less inhaled doses are recorded, for example, when the subject is approaching exacerbation. This is because the subject's ability to inhale is reduced. As used herein, the term "minimum inhalation volume" means the minimum inhalation volume recorded for inhalations performed using the first and / or second inhaler within a given (third) period. do.

The pressure changes associated with each inhalation are used to selectively or additionally measure inhalation duration. For example, the time from the initial pressure drop measured by the pressure sensor 12A, which coincides with the start of inhalation, to the return to the pressure when no inhalation is taking place is recorded. Shorter inhalation durations are recorded, for example, when the subject is approaching exacerbations. This is because the subject's ability to inhale for a long time is reduced. As used herein, the term "minimum inhalation duration" is the shortest inhalation duration recorded for inhalations performed using the first and / or second inhaler within a given (fourth) period. Means.

In one embodiment, the parameters include, for example, as a substitute for or in addition to the maximum inhalation flow rate and / or inhalation volume and / or inhalation duration, time vs. maximum inhalation flow rate. This time vs. maximum inhalation flow rate is recorded, for example, from the first pressure drop measured by the sensor system 12A, which coincides with the start of inhalation, until the pressure reaches the minimum corresponding to the maximum flow rate. Subjects at greater risk of exacerbations achieve maximum inhalation flow over time.

In a non-limiting example, the first and / or second inhaler is normally configured for inhalation such that each agent is administered for about 0.5 seconds following the onset of inhalation. The subject's inhalation, which reaches the maximum inhalation flow rate after the lapse of 0.5 seconds, such as after about 1.5 seconds, represents a partially imminent exacerbation.

The use detection system 12B provides inhalation by the subject (eg, each emergency inhalation by the subject when the inhaler is an emergency inhaler, or each maintenance inhalation by the subject when the inhaler is a maintenance inhaler). Configured to record.
In a non-constant example, the first inhaler 100 is a drug tank (not shown in FIG. 1) and a weighing assembly configured to weigh a single dose of drug from the drug tank (not shown in FIG. 1). ) And. The use detection system 12B is configured to record a single dose of drug metered by the metering assembly, where each metering represents an emergency inhalation performed by the subject using the first inhaler 100. Therefore, the inhaler 100 is configured to monitor the number of emergency inhalations of the drug. This is because a single dose of drug must be weighed by the weighing assembly before being inhaled by the subject. One non-limiting example of the configuration for weighing will be described in more detail with reference to FIGS. 18-22.

Optionally or additionally, the use detection system 12B registers each inhalation in various ways and / or based on additional or selective feedback apparent to those of skill in the art. For example, the use detection system 12B sets a predetermined threshold value associated with a successful inhalation when the feedback from the sensor system 12A indicates that inhalation by the subject is occurring (eg, a pressure measurement or flow rate). (When exceeded), configured to register inhalation by the subject. Further, in some examples, the use detection system 12B is such that inhaler switching or user input of an external device (eg, the touch screen of a smartphone) is manually performed by the subject before or during or after inhalation. It is configured to register the inhalation when it is inhaled.

Sensors (eg, pressure sensors) are included in the use detection system 12B, for example, to register each inhalation. In such an example, the use detection system 12B and the sensor system 12A are configured to perform the functions of their respective sensors (eg, pressure sensors), or both use detection and suction parameter detection (eg, common). Use the pressure sensor).

When the sensor is included in the use detection system 12B, the sensor is inhaled by the subject with a single dose of drug weighed by the weighing assembly, for example, as described in more detail with reference to FIGS. 18-22. It is used to confirm that or to evaluate the degree of inhalation.

In one embodiment, the sensor system 12A and / or the use detection system 12B has an acoustic sensor. The acoustic sensor in this embodiment is configured to detect noise generated when the subject inhales using their respective inhalers. The acoustic sensor consists of, for example, a microphone.

In a non-limiting example, each inhaler has a capsule that is arranged to rotate when the subject inhales with the device. The rotation of the capsule produces noise that can be detected by the acoustic sensor. That is, the rotation of the capsule provides a reasonably interpretable noise to guide the use and / or inhalation parameter data, such as rattling.

The algorithm, for example, to interpret acoustic data and measure data for use (when an acoustic sensor is included in the use detection system 12B) and / or to measure parameters for airflow during inhalation (acoustic sensor). Is included in the sensor system 12A).

For example, Colthorpe et al., "Adding Electronics to the Breezhler; Satisfying the Needs of Patients", Respiratory Drug Delivery, Respiratory Drug Delivery. Drug Delivery), 2018, an algorithm as described on pages 71-79 is used. Once the generated sound is detected, the algorithm processes the raw acoustic data to generate data for use and / or inhalation parameters.

The processor 14 included in the system 10 measures the number of emergency and / or inhalation inhalations during the first period and receives the measured parameters for each of the emergency and / or regular inhalations. In FIG. 1, the processor receives inhalation and parameter data from the use detection system 12B and the sensor system 12A, respectively, as indicated by the arrows between the sensor system 12A and the processor 14, and between the use detection system 12B and the processor 14. The processor 14 further uses a weighted model to determine the probability of exacerbation of respiratory disease based on the number of emergency inhalations and parameters, as described in more detail in FIGS. 3-17. It is configured to calculate.

In a non-limiting example, the processor 14 is arranged independently of the respective first and / or second inhalers. In this case, the processor 14 receives data on the number of emergency inhalations and parameters transmitted from the sensor system 12A and the use detection system 12B of the first and / or second inhalers. By processing the data in an external processing unit such as a processing unit of the external device, the battery life of the inhaler is advantageously maintained.

In another non-limiting example, the processor 14 is an integral part of the first and / or second inhaler, eg, of the first and / or second inhaler (not shown in FIG. 1). Housed in the main housing or in the top cap. In such an example, it is not necessary to rely on the connection to an external device. This is because the calculation of the probability of exacerbation of respiratory disease is done only inside the first and / or second inhaler. The first and / or second inhaler includes a user interface such as a light source, a screen, a loudspeaker, etc. for informing the subject of the result of the probability calculation. Rather than numerically indicating the probabilities, some examples use more intuitive means of informing the subject of the risk, such as using different colored light sources depending on the calculated value of the probability. That is, the first and / or second inhaler may take proactive steps, such as, for example, inhaling the first or second inhalation of the first aid, to reduce or eliminate the risk of exacerbations. prompt.

Some of the functions of the processor 14 are performed by the internal processing unit contained in the first and / or second inhaler, and other functions of the processor such as the probability calculation itself are performed by the external processing unit. It is also possible to.

More generally, the system 10 has, for example, a communication module configured to inform the subject and / or a healthcare professional such as a doctor (not shown in FIG. 1) of the calculated probabilities. .. The subject and / or physician then take appropriate steps based on the calculated probability of exacerbation of respiratory illness. For example, when a smartphone processing unit is included in the processor, it is used to inform healthcare professionals of the calculated probability of smartphone communication functions such as SMS, email, Bluetooth®, etc.

FIG. 2 shows a non-limiting example of a system 10 that calculates the probability of exacerbation of respiratory disease in a subject. A weighted model, selectively called a respiratory disease exacerbation risk prediction model, is used to calculate probabilities and the calculation results are provided to subjects and / or caregivers and / or healthcare professionals. To.

The system 10 includes a first inhaler 100, an external device 15 (eg mobile device), a public and / or dedicated network 16 (eg Internet, cloud network, etc.) and a personal data storage device 17. The external device 15 can be connected by, for example, a smartphone, a personal computer, a laptop personal computer, a wireless media device, a media streaming device, a tablet device, a wearable device, a Wi-Fi or a television capable of wireless communication, or another suitable Internet protocol. Includes equipment. For example, the external device 15 may be a Wi-Fi communication link, a Wi-MAX communication link, a Bluetooth® or Bluetooth smart communication link, a short range wireless communication (NFC) link, a mobile phone communication link, or a white space for television. TVWS) configured to transmit and / or receive RF signals through communication links or combinations thereof. The external device 15 transmits data to the personal data storage device 17 through the public and / or dedicated network 16.

The first inhaler 100 has a communication circuit such as a Bluetooth radio for transmitting data to the external device 15. The data include the number of inhalations and parameter data described above.

The first inhaler 100 also receives data, such as program instructions, operating system changes, information about a single dosage, warnings or notifications, approvals, etc., from, for example, an external device 15.

The external device 15 includes at least a portion of the processor 14, thereby processing and analyzing data on the number of inhalations and parameters. For example, the external device 15, as represented by block 18A, processes data such as data for calculating the probability of exacerbation of respiratory disease and stores such information in personal data for automatic storage. Provided to the device 17.

In some non-limiting examples, the external device 15 also processes the data, as represented by block 18B, to result in non-inhalation events and / or low inhalation events and / or good inhalation events and /. Or identify over-inhalation events and / or exhalation events. The external device 15 also identifies inadequate use, excessive use and optimal use by processing the data, as represented by the block 18C. By processing the data, the external device 15 estimates, for example, the number of single doses given and / or left, and the subject neglects to inhale the single dose as weighed by the weighing assembly. Specify the error status such as the error status related to the time stamp error flag indicating that. The external device 15 has a display and software for visually displaying the parameters used on the display through a GUI. The parameters used are stored as personalized data stored to predict future risks of exacerbations based on real-time data.

In some examples, it was described as being stored in the personal data storage device 17, but the probability of exacerbation of respiratory disease as shown by block 18A, the inhalation-free event as shown by block 18B. , Low inhalation event, good inhalation event, overinhalation event and / or exhalation event, and / or underuse event, overuse event and optimal use event as indicated by block 18C are also stored in the external device 15. obtain.

FIG. 3A is a flow chart of a method 20 according to an embodiment of the present invention. Method 20 is performed by a system such as system 10 shown in FIGS. 1 and / or 2. For example, one or more of the first and / or second inhalers and / or external devices 15 and / or personal data storage devices 17 are configured to perform all or part of method 20. .. That is, any combination of steps 22, 24 and 26 is performed by any combination of a first inhaler and / or a second inhaler and / or an external device 15 and / or a personal data storage device 17. Further, it should be understood that steps 22 and 24 are performed in any temporal order.

Method 20 comprises step 22 of measuring the number of first-aid inhalations of the first-aid drug made by the subject within the first period. In step 24, parameters are measured at least several times, eg, with respect to airflow during each emergency and / or periodic inhalation. In step 26, a weighted model is used to calculate the probability of exacerbation of asthma based on the number of emergency inhalations and parameters. The model is weighted so that the number of emergency inhalations is more significant than the parameter in the probability calculation.

Although not described in Method 20, the system 10 is configured to notify the user when the probability of exacerbation of asthma exceeds or falls below a threshold. For example, the system 10 is configured to determine whether the probability reaches or exceeds a predetermined upper limit and / or whether the probability reaches or is lower than a predetermined lower limit. Will be done. Accordingly, the system 10 switches the patient's treatment plan, for example, to a treatment plan created for a higher (lower) risk of exacerbation of asthma than the original treatment plan (eg, patient). (Through a message to the healthcare professional in charge of), configured to treat the patient.

System 10 informs the user of the probability of exacerbation of asthma through one or more techniques. For example, the system 10 displays a message on the display of the external device 15, sends the message to a healthcare professional or a third party related to the user, and activates an indicator (eg, a light or speaker) of the inhaler 100. Is configured to inform the user.

In the non-limiting example shown in FIG. 13A, the method further comprises step 23 of receiving input of an index relating to the pathology of the respiratory illness suffered by the subject. This input is used to improve the accuracy of exacerbation prediction, as described above.

In one embodiment, the method 20 has a step of outputting a prompt to the user and prompting the user for input. Prompts are output based on the number of inhalations and inhalation parameters, but not on indicators. For example, the prompt is output based on the initial probability of reaching or exceeding a predetermined threshold. In this way, the system prompts the user to enter an indicator based on the initial probability that an imminent exacerbation is possible. Then, as the user inputs the metric, the (overall) probability that takes the metric into account helps to confirm or justify the initial probability.

FIG. 3B is a flow chart and a timeline relating to the method according to another embodiment of the present invention. The timeline is the date of the predicted exacerbation (“[0] days”), 5 days prior to the exacerbation (“[-5] days”) and 10 days prior to the exacerbation (“[-10] days”). Is shown.

In FIG. 3B, block 222 represents an inhaler use notice that is considered a notice regarding the use of emergency and / or maintenance medications. Block 224 represents a flow rate notification corresponding to the parameters for airflow during inhalation. Block 225 represents "use" and "flow" notifications that are considered combined notifications based on inhaler use and inhalation parameters.

Block 226 represents a notification of the forecast. The notification of this prediction is based on the calculation of the initial probability described above. FIG. 3B shows that the questionnaire was presented at block 223 on [-10]. Presenting the questionnaire involves outputting a prompt to the user to enter an indicator through the questionnaire. Block 227 indicates that the result of the questionnaire shows that the risk of exacerbation remains even after the user inputs. This means that at block 230, the questionnaire will continue to be presented, or the user will be asked to enter the metric again, or will be asked to further enter the pathology of their respiratory illness. Block 231 represents a scenario in which the risk of exacerbation remains, for example, after the calculation of the overall probability described above, and in block 233, notification of the exacerbation prediction continues.

Block 228 represents a scenario in which the calculated risk of exacerbation returns to baseline based on user-entered indicators after the presentation of the block 223 questionnaire. Correspondingly, the risk notification ends at block 229.

Similarly, block 232 represents a scenario in which the risk of exacerbation returns to baseline after the continuous or additional questionnaire presentation of block 230 is complete. Although not shown in FIG. 3B (for simplicity of explanation), the risk notification ends at block 232 after the risk of exacerbation has returned to baseline.

More generally, the method 20 further comprises the step of preparing a first inhaler for administering the emergency drug to the subject. The first inhaler has a use detection system configured to detect inhalations made by the subject using the first inhaler.

The number and parameters of emergency inhalation are measured by the use detection system and sensor system included in the first inhaler for administering the emergency drug, respectively. The sensor system selectively or additionally measures parameters for airflow during regular inhalation of maintenance medication using a second inhaler, as previously described.

Here, the weighted model supporting the method according to one embodiment of the present invention was the result of a clinical trial. This will be explained next. The following examples should be considered as non-limiting examples for illustration purposes.

Proair marketed by Teva Pharmaceutical Industries, although the results of the study are generally applicable to other first-aid drugs administered using another type of device. Albuterol administered using ProAir Digihalaer® was used in a 12-week multicenter open-label study.

Patients (aged 18+) with asthma, which is prone to exacerbation, were included in the study. Patients used Proair Digihaler as needed (90 mcg of albuterol as a sulfate containing lactose carrier was inhaled 1-2 times every 4 hours).

The Digihaller's electronic module recorded parameters for each use, ie airflow between each inhalation and each inhalation, namely maximum inhalation flow rate, inhalation volume, time vs. maximum inhalation flow rate and inhalation duration. The data were downloaded from the inhaler along with clinical data and applied to machine learning algorithms to develop models for predicting imminent exacerbations.

The diagnosis of clinical asthma exacerbation (CAE) in this example is made by the American Thoracic Society / European Respiratory Society statement (HK Reddel et al.), Am J Respir Crit Care Med. 2009, Vol. 180 (1), pp. 59-99). It includes both "significant CAE" and "medium CAE".

Significant CAE is defined as CAE containing exacerbated asthma requiring administration and hospitalization of oral steroids (prednisone or its equivalents) for at least 3 days.
Normal CAE requires at least 3 days of dosing or hospitalization of oral steroids (prednisone or its equivalents).

The generalized model was evaluated by receiver operating characteristic (ROC) curve analysis, as described in more detail below with reference to FIG.

The purpose and primary endpoint of the study are parameters such as airflow, physical activity and sleep during CAE-preceding albuterol usage pattern and usage alone or during CAE-preceding inhalation captured by the Digihaller. It was to investigate in combination with other test data. This test provides the first successful attempt to develop a model for the prediction of CAE, which is derived from the use of an emergency drug inhaler equipped with an integrated sensor and capable of measuring inhalation parameters.

FIG. 4 shows three timelines showing different inhalation patterns recorded by their respective digital hallers for three different patients. The top timeline shows that the patient in question makes one inhalation at a time. The lowest timeline shows that the patient in question makes two or more consecutive inhalations per session. Here, the term "session" is defined as a series of inhalations in which successive inhalations are made at time intervals of up to 60 seconds. An intermediate timeline shows that the patient in question performs inhalation in different patterns. That is, the Digihaller is configured to not only record the number of emergency inhalations, but also the pattern of use.

It was found that 360 patients received one or more effective inhalations from the Digihaller. These 360 patients were included in the analysis. Of these patients, 64 experienced a total of 73 CAEs.
FIG. 5 is a graph 30 of the average number of emergency inhalations versus the number of days from exacerbation of asthma. FIG. 5 shows data for a risk period of 14 days before and after the date of exacerbation. The straight line 32 corresponds to the average number of emergency inhalations per day during the risk period. The straight line 32 is located higher on the Y-axis than the average number of emergency inhalations per day outside the reference risk period, represented by the straight line 34. This indicates that the average number of emergency inhalations per day increases as the risk of exacerbation increases. For reference, FIG. 5 further shows the average number of emergency inhalations per day for patients who did not experience a baseline exacerbation, represented by straight line 36.

FIG. 6 is another graph 30 of the average number of emergency inhalations vs. the number of days since the exacerbation of asthma. FIG. 6 shows data within a period of 50 days before and after the date of exacerbation. FIG. 6 shows a significant increase in the number of times the emergency inhaler was used as the day of exacerbation approached, compared to the average number of emergency inhalations per day outside the reference risk period, represented by the straight line 34. Shows that to do.

FIG. 7 is a four graph of the rate of change vs. the number of days since exacerbation of asthma with respect to each reference value for the number of emergency inhalations and various parameters for airflow.

Graph 40 is a plot of the rate of change of the number of emergency inhalations relative to the reference value (outside the risk period) vs. the number of days since the exacerbation of asthma. It was found that the number of emergency inhalations increased by 90% from the reference value immediately before the exacerbation.

Graph 42 is a plot of the rate of change vs. the number of days since the exacerbation of asthma with respect to the reference value of the minimum maximum inhalation flow rate per day. Graph 42 shows that the minimum daily maximum inhalation flow rate generally decreases in the days leading up to exacerbations. It was found that the minimum maximum inhalation flow rate per day decreased by 12% from the reference value immediately before the exacerbation.

Graph 44 is a plot of the rate of change vs. the number of days since exacerbation of the minimum daily inhalation dose relative to the reference value. Graph 44 shows that the minimum daily inhalation dose generally decreases in the days leading up to exacerbations. It was found that the minimum daily inhalation amount decreased by 20% from the reference value immediately before the exacerbation.

Graph 46 is a plot of the rate of change vs. the number of days since exacerbation with respect to the reference value of the minimum inhalation duration per day. Graph 46 shows that the minimum daily inhalation duration generally decreases in the days leading up to exacerbations. It was found that the minimum inhalation duration per day decreased by 15% to 20% from the reference value immediately before the exacerbation.

In constructing the first weighted predictive model, it was found that the strongest predictor of asthma exacerbation was the average number of emergency inhalations per day, especially within the 5-day period prior to CAE. .. It was found that the parameters related to airflow, namely the maximum inspiratory flow rate and / or the inspiratory volume and / or the inspiratory duration, also had significant predictors.

In the first weighted model, the most significant characteristics in calculating the probability of exacerbation of asthma are 61% for emergency inhalation, 16% for inhalation tendency, 13% for maximum inhalation flow rate, and 8% for inhalation volume. , It was found that the use of albuterol at night was 2%. Such inhalation characteristics were collected by the Digihaller and recorded trends over time for maximum inhalation flow rate, time vs. maximum inhalation flow rate, inhalation volume, inhalation duration, nocturnal use and their parameters over time.

Inhalation tendency is an artificially created or designed parameter, such as the rate of change in inhalation volume today compared to the last 3 days. Another example is the rate of change in the number of emergency inhalations today compared to the last 3 days. In these examples, each trend is a variation in the inhalation volume and the number of emergency inhalations, rather than the inhalation volume and the number of emergency inhalations themselves.

Based on the above results, a first weighted predictive model was developed to calculate the probability of exacerbation of asthma. Supervised machine learning techniques and gradient boosting were used to solve the classification problem (whether exacerbations will occur in the next x days (exacerbation period)).

Gradient boosting techniques are well known in the prior art. J.H. Friedman, Computational Statistics & Data Analysis, 2002, Vol. 38 (4), pp. 376-378, and J.H. Friedman et al. (JH). Friedman et al.), The Annals of Statistics, 2000, Vol. 28 (2), pp. 337-407. It produces a predictive model in the form of an ensemble (multiple learning algorithms) of basic predictive models consisting of decision trees (tree-shaped models and their possible consequences). It is a single powerful learning in an iterative way by using an optimization algorithm to minimize some suitable loss function (the function of the difference between the estimated value and the true value for an example of the data). Form a person model. The optimization algorithm uses a training set of known values of the response variables (whether exacerbations will occur in the next x days) and their corresponding predicted values (list of characteristics and designed characteristics) to the loss function. Calculate the expected value of. The learning process continuously fits into the new model and provides more accurate predictions of the response variables.

Table 1 exemplifies the factors included in the first weighted predictive model, along with their relative weighting to each other.

Although an important factor in the predictive model for calculating the probability of exacerbation of imminent asthma is the tendency for the number of emergency inhalations and the number of emergency inhalations, the predictive model is associated with this regarding the airflow between inhalations. It became more reliable by supplementing the parameters.
FIG. 8 shows a receiver operating characteristic (ROC) curve analysis of a model that evaluates the quality of the model by plotting the true positive rate against the false positive rate. This first weighted predictive model used the relevant characteristics described above to predict an imminent exacerbation over the next 5 days with an AUC value of 0.75. When using the characteristic based only on the number of emergency inhalations, the AUC value is 0.69.

Therefore, although the effect on probability is less than the number of emergency inhalations, the parameters for airflow during inhalation, like other factors other than the number of emergency inhalations, determine the accuracy of calculating the probability of exacerbation of asthma. It shows that it is a significant factor to raise.

A second weighted predictive model was developed using the same data to improve the first weighted predictive model. Biological parameters were included in the model. In particular, case report (CRF) data such as medical history, Modimas index (BMI) and blood pressure were combined with Digihaller data and applied to machine learning algorithms to improve predictive models.

The algorithm was trained not only on age, BMI, blood pressure and number of exacerbations and hospitalizations over the last 12 months, but also on patient-specific inhalation information collected by Digihaller. Comparisons between reference and unpredicted characteristics, and trends in changes in those characteristics, were applied to supervised machine learning algorithms. Four-fold cross-validation techniques were used to compare performance metrics, and gradient boosting was selected as the optimal algorithm. As before, the generalized model was evaluated by analysis of the receiver operating characteristic curve (ROC) and the area under the curve (AUC).

Table 2 exemplifies the factors included in the second weighted predictive model, along with their relative weighting to each other.

A second weighted predictive model predicted an imminent exacerbation for the next 5 days with an AUC value of 0.83. The second weighted predictive model has a sensitivity of 68.8% and a specificity of 89.1%. Thus, the second weighted predictive model may be an improved asthma exacerbation predictive model over the first weighted predictive model with an AUC value of 0.75 described above. Shown. The additional refinement of the second weighted predictive model is at least partially attributed to the introduction of biological parameters.

More generally, the first period in which the number of emergency inhalations should be measured is 1 to 15 days, preferably 3 to 8 days. Monitoring the number of emergency inhalations over the first period is particularly useful in calculating the probability of exacerbation of asthma.

When the inhalation parameter includes a maximum inspiratory flow rate, Method 20 further calculates a maximum inspiratory flow rate, such as a minimum or average maximum inspiratory flow rate, from the maximum inspiratory flow rate measured for inhalations made during the second period. Have a step. The term "second" with respect to the second period is intended to distinguish the period of sampling the maximum inhalation flow rate from the first period in which the number of emergency inhalations is sampled. The second period may at least partially overlap the first period, or the first and second periods may coincide.

Thus, step 26 of calculating the probability of exacerbation of asthma is based in part on the minimum or average maximum inhalation flow rate. The second period is 1 to 5 days, for example, 1 day. The second period is selected according to the time required to collect data for the maximum inhalation flow of appropriate values, in a manner similar to the discussion described for the first period above.

The calculation of the probability of exacerbation of asthma is based, for example, on the change with respect to the maximum inspiratory flow rate, which is the basis for the partially or average maximum inspiratory flow rate, as shown in graph 42 of FIG.

For the improved accuracy of exacerbation prediction, the change to the maximum inspiratory flow rate, which is the basis for the minimum or average maximum inspiratory flow rate, is, for example, 10% or more (50% or more or 90% or more, etc.). be. Reference values are calculated using the minimum daily maximum inhalation flow measured over a period of no exacerbations, eg, 1-20 days, preferably 10 days. Selectively or additionally, the minimum or average maximum inspiratory flow rate is evaluated relative to the absolute value.

Method 20 further comprises the step of calculating an inhalation amount, such as a minimum or average inhalation amount, from the inhalation amount measured for inhalations made within the third period. The term "third" with respect to the third period distinguishes the period in which the inhalation volume is sampled from the first period in which the number of emergency inhalations is sampled and the second period in which the maximum inhalation flow rate data is sampled. It is intended. The third period can at least partially overlap the first and / or second period, or the third period coincides with at least one of the first and second periods. May be.

Thus, step 26 of calculating the probability of exacerbation of asthma is based in part on the minimum or average inhalation dose. The third period is, for example, 1 to 5 days. The third period is selected according to the time required to collect the minimum inhalation amount data of appropriate values in a manner similar to the discussion described for the first period above.

The calculation of the probability of exacerbation of asthma is based in part on changes to the reference inhalation volume of the minimum or average inhalation dose, as shown in graph 44 of FIG. 7, for example.

For the improved accuracy of exacerbation prediction, the change in the minimum or average inhalation volume relative to the reference inhalation volume is, for example, 10% or more (50% or more or 90% or more, etc.). Reference values are calculated using the minimum daily inhalation dose measured over a period of no exacerbations, eg 1-10 days. Selectively or additionally, the minimum or average maximum inspiratory flow rate is evaluated relative to the absolute value.

Method 20 further comprises the step of calculating the minimum or average inhalation duration from the inhalation duration measured for inhalation over a fourth period. The term "fourth" with respect to the fourth period refers to the period in which the minimum inhalation duration is sampled, the first period in which the number of emergency inhalations is sampled, and the second period in which the maximum inhalation flow rate data is sampled. It is intended to distinguish from the third period during which inhalation volume data is sampled. The fourth period may at least partially overlap with the first and / or second and / or third period, or the fourth period may overlap with the first and second period and It may coincide with at least one of the third period.

Thus, step 26 of calculating the probability of exacerbation of asthma is based in part on the minimum or average inhalation duration. The fourth period is 1 to 5 days, for example, 1 day. The fourth period is selected according to the time required to collect the minimum inhalation duration data of appropriate reference values in a manner similar to the discussion described for the first period above.

The calculation of the probability of exacerbation of asthma is based, for example, on a change with respect to the inhalation duration, which is partly the basis for the minimum or average inhalation duration, as in Graph 46 of FIG.

For the improved accuracy of exacerbation prediction, the change in inhalation duration as the basis for the minimum or average inhalation duration is, for example, 10% or more (50% or more or 90% or more, etc.). be. Reference values are calculated using a minimum daily inhalation duration measured over a period of no exacerbations, eg, 1-20 days. Selectively or additionally, the minimum or average maximum inhalation duration is evaluated relative to the absolute value.

Another clinical trial was conducted to better understand the factors that influence the prediction of exacerbations of COPD. The embodiments described below should be regarded as a non-limiting example for one explanation.

Proair marketed by Teva Pharmaceutical Industries, although the results of the study are generally applicable to other first aid drugs administered using another type of device. Albuterol administered using ProAir Digihaler® was used in a 12-week multicenter open-label study.

The digital haller was in a state where it was possible to record the total number of inhalations, the maximum inhalation flow rate, the time vs. the maximum inhalation flow rate, the inhalation amount and the inhalation duration. The data was downloaded from Digihaller's electronic module at the end of the test.

Imminent exacerbation of COPD (AECOPD) was the primary criterion for the outcome of this study. In this study, AECOPD consists of the onset of either "serious AECOPD" or "ordinary AECOPD". In this study, "mild AECOPD" was not used as a criterion for AECOPD.

Critical AECOPD is for treatment with systemic corticosteroids (SCS, at least 10 mg prednisone equivalent above baseline) and / or systemic antibiotics for at least 2 consecutive days and for the treatment of AECOPD. It is defined as an event that involves exacerbation of respiratory illness that requires hospitalization.

Normal AECOPD is treated with SCS (at least 10 mg of prednisone equivalent above baseline) and / or systemic antibiotics for at least two consecutive days and (telephone call, hospital visit, emergency treatment visit or emergency). It is defined as an event involving exacerbation of respiratory illness that requires unscheduled contact for treatment of AECOPD (such as a treatment visit) but does not require hospitalization.

Patients with COPD (40+) were recruited for the study. Patients used Proair Digihaler as needed (90 mcg of albuterol as a sulfate containing lactose carrier was inhaled 1-2 times every 4 hours).

The following selection criteria were requested. That is, the patient develops at least one of moderate or significant AECOPD over the last 12 months prior to screening, and in addition to SABA, at least one of LABA, ICS / LABA, LAMA or LABA / LAMA. Having received one dose, it was possible to demonstrate proper use of albuterol from Digihaller and discontinue use of all other emergency or maintenance SABA or short-acting antimuscarinic agents during the study. , Being able to switch them to the digital hallers provided for testing.

In the investigator's opinion, the patient has an underlying pulmonary disorder that causes confusion other than COPD, for example, if there are clinically significant symptoms (under treatment or untreated) that prevent participation in the trial. If the study drug was used within the 5 half-life after discontinuation of medication or within the longer of the two visits per month, congestive heart failure had developed. If you were pregnant or during the lactation period, or if you were likely to become pregnant during the trial, you were excluded from the trial.

Two subpopulations, consisting of about 100 patients, have accelerometers on their ankles to measure physical activity (Total Daily Steps (TDS)) or sleep disorders (sleep disorder index). (Sleep Disturbance Index; SDI)) was requested to be worn on the wrist to measure.

A common factor of interest for first aid is
(1) Total number of inhalations per day preceding the maximum of AECOPD (2) Number of days when the number of times of use of albuterol preceding the maximum of AECOPD increases (3) Number of times of use of albuterol for 24 hours preceding AECOPD there were.

About 400 patients were enrolled. This resulted in 366 valuable patients who completed the study. Effective inhalation by 336 Digihallers was recorded. Further details in this regard are shown in Table 3.

Ninety-eight patients who completed the study experienced an event of AECOPD and used a digital haller. A total of 121 average / significant AECOPD events were recorded. Further details are shown in Table 4.

Of the 366 patients who completed the study, 30 (8%) did not use the inhaler at all, and 268 (73%) inhaled up to an average of 5 times a day, 11 (3). %) Patients inhaled 10 times or more a day on average.

FIG. 9 is a graph 30b of the average number of emergency inhalations per subject versus the number of days since exacerbation of COPD. FIG. 9 shows data within a 14-day risk period before and after the date of exacerbation. The straight line 32b corresponds to the average number of emergency inhalations per day during the risk period. The straight line 32b is located higher on the Y-axis than the average number of emergency inhalations per day outside the reference risk period, represented by the straight line 34b. This indicates that the average number of emergency inhalations per day increases as the risk of exacerbation increases. For reference, FIG. 9 further shows the average number of emergency inhalations per day for patients who did not experience a baseline exacerbation, represented by straight line 36b.

FIG. 10 is another graph 30b of the average number of emergency inhalations per subject versus the number of days since exacerbation of COPD. FIG. 10 shows data within a 30-day risk period before and after the date of exacerbation. FIG. 10 shows a significant increase in the number of times the emergency inhaler was used as the day of exacerbation approached, compared to the average daily number of emergency inhalations outside the reference risk period represented by the straight line 34b. Shows that to do.

The data show that the number of emergency drug inhalations increases during the approximately two weeks prior to the onset of exacerbations. The rate of increase is small for about a week prior to the onset of exacerbations. Table 5 shows further details regarding the association between increased number of first aid use and AECOPD.

[1] For patients who have experienced an AECOPD event, the use of albuterol is made before the maximum symptom of the event is reached. For patients who have experienced multiple events, only the first event is included in the analysis. The standard use of albuterol is defined as the average number of inhalations during the first 7 days of the study. If no inhalation is made during this first 7 days, the first available inhalation after the 7 days is used. If no inhalation is performed during the test period, the reference value will be zero.
[2] All of the inference statistics, odds ratios, p-values, and C-statistics for goodness of fit are calculated as test variables using the increasing number of albuterol usages and reference albuterol usages from the logistic regression model. To. Odds ratios greater than 1 are such that patients with routine albuterol use exceeding the reference value by as much as 20% experience AECOPD events more than patients with routine albuterol use not exceeding the reference value by 20%. Indicates that there is a high possibility of doing so. Patients who experienced AECOPD between days 1-7 of the study were excluded from the analysis.

FIG. 11 is a graph 40b of the number of days since the exacerbation of the average maximum inhalation flow rate vs. COPD per subject. FIG. 11 shows data for a risk period of 14 days before and after the date of exacerbation. The straight line 42b is located slightly higher on the Y-axis than the average maximum inspiratory flow rate outside the reference risk period represented by the straight line 44b, but this difference does not appear to be very significant. FIG. 11 further shows the average maximum inhalation flow rate for patients who did not experience a reference exacerbation, represented by a straight line 46b.

FIG. 12 is another graph 60b of the average maximum inhalation flow rate per subject vs. the number of days since the exacerbation of COPD. FIG. 12 shows data for 30 days before and after the date of exacerbation. FIG. 12 shows a relatively stable, low average maximum inspiratory flow rate prior to the occurrence of exacerbations.

FIG. 13 is a graph 60b of the number of days since the exacerbation of COPD vs. average inhalation per subject. FIG. 13 shows data within a risk period of 14 days before and after the date of exacerbation. The straight line 62b corresponds to the average inhalation volume during the risk period. The straight line 62b is located at a lower position on the Y-axis than the average inhalation amount within the reference risk period represented by the straight line 64b. FIG. 13 further shows the average inhalation volume for subjects who did not experience a reference exacerbation, represented by a straight line 66b.

FIG. 14 is another graph 60b of the average inhalation volume per subject versus the number of days since exacerbation of COPD. FIG. 14 shows data for a period of 30 days before and after the date of exacerbation.

FIG. 15 is a graph 70b of average inhalation duration per subject vs. days from exacerbation of COPD. FIG. 15 shows data within a risk period of 14 days before and after the date of exacerbation. The straight line 72b corresponds to the average inhalation duration within the risk period. The straight line 72b is located lower on the Y-axis than the average inhalation duration outside the reference risk period, represented by the straight line 74b. FIG. 15 further shows the mean inhalation duration for subjects who did not experience a reference exacerbation, represented by straight line 76b.

FIG. 16 is another graph 70b of average inhalation duration per subject vs. days from exacerbation of COPD. FIG. 16 shows data within a period of 30 days before and after the date of exacerbation.

13-16 show a relatively long-term (demonstrated over about 30 days) linear reduction in inhalation volume and duration of inhalation prior to AECOPD.

Table 6 compares the inhalation parameters and the number of inhalations of the first aid recorded for patients inside and outside the window of ACECOPD for ± 14 days with the inhalation parameters and the number of inhalations of the first aid recorded for patients who did not experience AECOPD. It was done.

The standard daily average number of inhalations of albuterol for patients who experienced exacerbations was slightly higher than for patients who did not experience exacerbations, and the standard average inhalation volume and continued inhalation for patients who experienced exacerbations. The time was slightly lower than in patients who did not experience exacerbations. Within the ± 14-day AECOPD window, patients had an average number of albuterol inhalations per day above reference values (outside the ± 14-day AECOPD window) and even compared to patients who did not experience AECOPD. It became more.

In contrast to the predictive model for asthma exacerbations described above, the strongest predictors of COPD exacerbations are airflow parameters such as maximum inhalation flow rate and / or inhalation volume and / or inhalation duration. I understand. It was also found that the number of emergency inhalations included a significant predicted value.

Based on the above results, a weighted model was developed to calculate the probability of exacerbation of COPD. A supervised machine learning technique, Gradient Boosting Trees, was used to solve the classification problem (whether COPD exacerbations will occur in the next x days (exacerbation period)).

Table 7 shows an exemplary list of factors contained in the weighted model, along with their relative weighting to each other.

The generalized model was evaluated by receiver operating characteristic (ROC) curve analysis. The most significant factor in the predictive model for calculating the probability of an imminent exacerbation of COPD is the inhalation parameter, which was further confirmed by supplementing this with data on the number of emergency inhalations. ..
FIG. 17 shows a receiver operating characteristic (ROC) curve analysis of the model, assessing the quality of the model by plotting the true positive rate against the false positive rate. This model predicted an imminent exacerbation for the next 5 days with an area under the ROC curve (AUC) value of 0.77.

When predicting exacerbations of COPD, the number of emergency inhalations is a significant factor in improving the accuracy of the probability calculation of exacerbations, even though it has a smaller effect on overall probability than inhalation parameters.

18-22 show non-limiting examples of inhalers included in system 10.

FIG. 18 is a perspective view of the first inhaler 100 according to a non-limiting example. The inhaler 100 is, for example, a self-inhalation type inhaler. The inhaler 100 has an upper cap 102, a main housing 104 and / or a mouthpiece 106 and / or a mouthpiece cover 108 and / or an electronic module 120 and / or a vent 126. The mouthpiece cover 108 is hinged to the main housing 104 so that the mouthpiece 106 can be exposed or stowed by opening and closing the mouthpiece cover. Although hinged coupling is used in this example, the mouthpiece cover 108 can also be coupled to the inhaler 100 by another type of coupling. Further, in this example, the electronic module 120 is housed in the upper cap 102 at the top of the main housing 104, but the electronic module 120 is integrated with the main housing 104 of the inhaler 100 and / or of the main housing 104. It may be housed inside.

FIG. 19 is a vertical sectional view of the inhaler 100 shown in FIG. Inside the main housing 104, the inhaler 100 includes a drug tank 110 (eg, hopper), bellows 112, bellows spring 114, yoke (not shown), dosing cup 116, dosing chamber 117, crusher 121 and flow path 119. have. The drug tank 110 has a drug such as a dry powder drug for administration to the subject. When the mouthpiece cover 108 is moved from the closed position to the open position, the bellows 112 supplies a single dose of the drug from the drug tank to the dosing cup 116. Then, the subject inhales through the mouthpiece 106 and inhales one dose of the drug.

The airflow generated by the subject's inhalation causes the crusher 121 to aerosolize a single portion of the drug by crushing the drug aggregates in the dosing cup 116. The crusher 121 is configured to aerosolize the drug when the airflow through the flow path 119 reaches or exceeds a particular velocity, or has a velocity within a particular range. When aerosolized, a single dose of drug flows from the dosing cup 116 through the flow path 119 into the dosing chamber 117 and is then delivered to the subject from the mouthpiece 106. When the airflow through the flow path 119 does not reach or exceed a certain velocity, or the velocity is not within a certain range, the drug remains in the dosing cup 116. If the drug in the dosing cup 116 was not aerosolized by the crusher 121, the next dose of drug will not be delivered from the drug tank 110 when the mouthpiece cover 108 is subsequently opened. That is, the single dose remains in the dosing cup until the single dose is aerosolized by the crusher 121. When one dose of the drug is administered, the confirmation that the medication has been taken is stored in the memory as the medication confirmation information in the inhaler 100.

When the subject is inhaling through the mouthpiece 106, air enters through the ventilation openings and creates an airflow to supply the drug to the subject. The flow path 119 extends from the dosing chamber 117 to the end of the mouthpiece 106 and includes the dosing chamber 117 and the inner portion of the mouthpiece 106. The dosing cup 116 is inside or adjacent to the dosing chamber 117. Further, in the inhaler 100, the total number of doses of the drug in the drug tank 110 is initially set, and each time the mouthpiece cover 108 is moved from the closed position to the open position, the total number of doses is decremented by one. It has a configured dosing count counter 111.

The upper cap 102 is attached to the main housing 104. The upper cap 102 is attached to the main housing 104, for example, with one or more clips that can engage the recesses of the main housing 104. When joined, the upper cap 102 overlaps a portion of the main housing 104, for example, providing a substantially airtight seal between the upper cap 102 and the main housing 104.

FIG. 20 is an exploded perspective view of the inhaler 100 shown in FIG. 18, showing a state in which the upper cap 102 is removed and the electronic module 120 is exposed. As shown in FIG. 20, the upper surface of the main housing 104 has one or more (eg, two) orifices 146. One orifice 146 is configured to accommodate the slider 140. For example, when the upper cap 102 is attached to the main housing 104, the slider 140 projects from the top surface of the main housing 104 through one orifice 146.

FIG. 21 is an exploded perspective view of the upper cap 102 and the electronic module of the inhaler 100 shown in FIG. As shown in FIG. 21, the slider 140 forms an arm 142, a stopper 144 and a distal end 145. The distal end 145 forms the bottom of the slider 140. The distal end 145 of the slider 140 is configured to abut the yoke inside the main housing 104 (eg, when the mouthpiece cover 108 is in the closed or partially open position). The distal end 145 is configured to abut on the top surface of the yoke when the yoke is in any of the radial directions. For example, the top surface of the yoke has multiple apertures (not shown), and the distal end 145 of the slider 140 hits the top surface of the yoke, for example, whether or not one aperture is aligned with the slider 140. It is configured to touch.

The upper cap 102 has a slider spring 146 and a slider guide 148 configured to receive the slider 140. The slider spring 146 is located inside the slider guide. The slider spring 146 engages the inner surface of the upper cap 102 and engages the upper part (eg, the proximal end) of the slider 140. When the slider 140 is placed inside the slider guide 148, the slider spring 146 is partially compressed between the top of the slider 140 and the inner surface of the top cap 102. For example, the slider spring 146 is configured such that the distal end 145 of the slider 140 is in contact with the yoke when the mouthpiece cover 108 is closed. The distal end 145 of the slider 145 is also configured to be in contact with the yoke while the mouthpiece cover 108 is open or closed. The stopper 144 of the slider 140 is engaged with the slider guide 148 so that, for example, the slider 140 is held in the slider guide 148 through the opening and closing of the mouthpiece cover 108 and vice versa. The stopper 144 and the slider guide 148 are configured to limit the vertical (eg, axial) movement of the slider 140. This limited movement distance is shorter than the limited movement distance of the vertical movement of the yoke. That is, when the mouthpiece cover 108 is moved to a fully open position, the yoke continues to move up and down towards the mouthpiece 106, but the stopper 144 stops the up and down movement of the slider 140, thereby the slider. The distal end 145 of 140 no longer contacts the yoke.

More generally, the yoke is mechanically coupled to the mouthpiece cover 108, compressing the bellows spring when the mouthpiece cover 108 is opened from the closed position, after which the mouthpiece cover 108 reaches the open position. At this time, the compressed bellows spring 114 is opened, whereby the bellows 112 is configured to supply a single dose of the drug from the drug tank 110 to the dosing cup 116. The yoke is in contact with the slider 140 when the mouthpiece cover 108 is in the closed position. The slider 140 is provided so as to be moved by the yoke when the mouthpiece cover 108 is opened from the closed position and away from the yoke when the mouthpiece cover 108 is in the fully open position. This configuration is considered a non-limiting example of the weighing assembly already described above. This is because the mouthpiece cover 108 is opened to measure a single dose of the drug.

The movement of the slider 140 during the metering of the drug causes the slider 140 to engage the switch 130 to activate the switch 130. The switch 130 activates the electronic module 120 to record the metering of the drug. The slider 140 and the switch 130, along with the electronic module 120, correspond to the non-limiting example of the use detection system 12B described above. In this example, the slider 140 is thereby regarded as a means by which the use detection system 12B is configured to record a single dose of drug by the weighing assembly, each weighing by which the subject uses the inhaler 100. Represents the inhalation performed.

By operating the switch 130 by the slider 140, for example, the electronic module 120 transitions from the first power state to the second power state, and detects inhalation from the mouthpiece 108 by the subject.

The electronic module 120 has a printed circuit board (PCB) assembly 122 and / or a switch 130 and / or a power source (eg, battery 126) and / or a battery holder 124. The PCB assembly 122 has surface mount components (not shown) such as sensor systems 128 and / or wireless communication circuits 129 and / or switches 130 and / or one or more light emitting diodes (LEDs). ing. The electronic module 120 has a controller (eg, a processor) and / or a memory. The controller and / or memory is physically different from the components of the PCB 122. Alternatively, the controller and memory consist of part of another chipset mounted on the PCB 122, for example the wireless communication circuit 129 includes a controller and / or memory for the electronic module 120. The controller of the electronic module 120 includes a microcontroller, a programmable logic device (PLD), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or any suitable processing device or control circuit.

The controller reads the data from the memory and records the data in the memory. Memory includes any form of suitable memory, such as non-removable memory and / or removable memory. Non-removable memory includes random access memory (RAM), read-only memory (ROM), hard disk, or any other form of storage. Removable memory includes subscriber ID module (SIM) cards, memory sticks, secure digital (CD) memory cards and the like. The memory is located inside the controller. The controller also reads data from a memory that is not physically located inside the electronic module 120, such as on a server or smartphone, and records the data in it.

The sensor system 128 has one or more sensors. The sensor system 128 is an example of the sensor system 12A. The sensor system 128 is one or more different types of sensors such as, for example, one or more pressure sensors and / or temperature sensors and / or humidity sensors and / or direction sensors and / or acoustic and / or optical sensors. have. One or more pressure sensors include barometric pressure sensors (eg, atmospheric pressure sensors) and / or differential pressure sensors and / or absolute pressure sensors and / or the like. The sensor uses the technology of microelectromechanical systems (MEMS) and / or nanoelectromechanical systems (NEMS). The sensor system 128 is configured to give the controller of the electronic module 120 a momentary measurement (eg, a pressure measurement) and / or an aggregated measurement (eg, a pressure measurement) over a period of time. There is. As shown in FIGS. 19 and 20, the sensor system 128 is located outside the flow path 119 of the inhaler 100, but is pneumatically coupled to the flow path.

The controller of the electronic module 120 receives a signal corresponding to the measured value from the sensor system 128. The controller calculates or determines airflow metrics using the signal received from the sensor system 128. The airflow metrics represent the profile of the airflow through the flow path 119 of the inhaler 100. For example, when the sensor system 128 records a pressure change of 0.3 kilopascals (kPa), the electronic module 120 corresponds to an air flow of about 45 liters / minute (Lpm) through the flow path 119. Calculate that.

FIG. 22 is a graph of air flow rate vs. pressure. The air flow rate and profile shown in FIG. 22 are merely exemplary, and the calculated speed depends on the size, shape and design of the inhaler 100 and its components.

The controller of the electronic module 120 inhales personalized data in real time by comparing the signal and / or calculated airflow metrics received from the sensor system 128 with a threshold or numerical range of one or more. Generated as part of an assessment of how 100 was used and / or whether its use could result in complete administration of the drug. For example, when the calculated airflow metrics correspond to inhalation with an air flow below a certain threshold, the electronic module 120 has no inhalation from the mouthpiece 106 of the inhaler 100, or the mouthpiece 106. It is determined that the inhalation from the mouth is insufficient. Also, when the calculated airflow metrics correspond to inhalation with an air flow rate above a certain threshold, the electronic module 120 determines that inhalation from the mouthpiece 106 of the inhaler 100 is excessive. Also, when the calculated airflow metrics correspond to inhalation with air flow within a certain range, the electronic module 120 may have good inhalation or result in complete dosing. Judged as having sex.

Pressure measurements and / or calculated airflow metrics represent the quality or intensity of inhalation from the inhaler 100. For example, when compared to a particular threshold or numerical range, the measurements and / or metrics classify the relevant inhalation into certain event types such as good inhalation events, low inhalation events, no inhalation events or overinhalation events. Used to do. Inhalation classification is a useful parameter recorded as personalized data of the subject.

Non-inhalation events are associated with pressure measurements and / or airflow metrics below a certain threshold, such as air flow below 30 Lpm. The non-inhalation event occurs when the subject does not inhale from the mouthpiece 106 after opening the mouthpiece cover 108 and during the measurement cycle. Also, a non-inhalation event is a subject, such as when inhalation by the subject has generated insufficient airflow to activate the crusher 121, i.e., to aerosolize the drug in the dosing cup 116. It occurs when a person's inhalation is insufficient to ensure proper administration of the drug through channel 119.

Low inhalation events are associated with pressure measurements and / or airflow metrics within a certain numerical range, such as 30 Lpm to 45 Lpm. In the low inhalation event, the subject inhaled from the mouthpiece 106 after opening the mouthpiece cover 108, and even by inhalation by the subject, only at least one part of the single dose of the drug was administered through the channel 119. It only happens when it is. That is, inhalation is insufficient to activate the crusher 121 to aerosolize at least one portion of the drug from the dosing cup 116.

Good inhalation events are associated with pressure measurements and / or airflow metrics above low inhalation events such as 45 Lpm to 200 Lpm. In a good inhalation event, the subject inhales from the mouthpiece 106 after opening the mouthpiece cover 108, and by the subject's efforts, the crusher 121 is activated to take all of the single dose of the drug in the dosing cup 116. Occurs when sufficient inhalation is made to ensure proper administration of the drug through the flow path 119 by the subject's efforts, such as when sufficient airflow is generated for aerosolization.

Excessive inhalation events are associated with pressure measurements and / or airflow metrics above good inhalation events, such as air flow rates above 200 Lpm. The over-inhalation event occurs when the subject inhales beyond the normal operating parameters of the inhaler 100. Over-inhalation events also occur when the inhaler 100 is not properly positioned or held during use, even if the subject inhales within the normal range. For example, the calculated air flow rate exceeds 200 Lpm when the vent is blocked or blocked (eg, by one finger or thumb) while the subject is inhaling through the mouthpiece 106. ..

Any suitable threshold or numerical range is used to classify a particular event. Some or all events are used. For example, a non-inhalation event is associated with an air flow rate below 45 Lpm, and a good inhalation event is associated with an air flow rate in the range of 45 Lpm to 200 Lpm. Therefore, low inhalation events may not be used at all.

The pressure measurements and / or the calculated airflow metrics also represent the direction of the airflow through the flow path 119 of the inhaler 100. For example, when the pressure measurement reflects a negative pressure change, the pressure measurement represents the airflow exiting the mouthpiece 106 through the flow path 119. When the pressure measurement reflects a positive pressure change, the pressure measurement represents the airflow entering the mouthpiece 106 through the flow path 119. Therefore, pressure measurements and / or airflow metrics are used to determine if the subject is expelling air into the mouthpiece 106, whereby the subject is not using the inhaler 100 correctly. Let me know.

The inhaler 100 has a spirometer or a device that performs a similar operation to enable measurement of metrics related to lung function. For example, the inhaler 100 makes measurements to obtain metrics related to the subject's vital capacity. A spirometer or a device with similar behavior measures the volume of air inhaled and / or exhaled by the subject. A tooth activity meter or similar operation device uses a pressure transducer, ultrasound or water level gauge to detect changes in the volume of sucked and / or exhaled air.

Personalized data collected from the use of the inhaler 100 or calculated based on the use of the inhaler 100 (eg, pressure metrics, airflow metrics, lung function metrics, medication confirmation information, etc.) may be further enhanced. , (Partially or wholly) calculated and / or evaluated by an external device.
More generally, the radio communication circuit 129 of the electronic module 120 has not only a transmitter and / or a receiver (eg, a transceiver) but also an additional electrical circuit. For example, the wireless communication circuit 129 has a Bluetooth chipset (for example, a Bluetooth low power chipset), a Zigbee chipset, a thread chipset, and the like. Thus, the electronic module 120 provides personalized data such as pressure measurements and / or airflow metrics and / or lung function metrics and / or medication confirmation information and / or other conditions regarding the use of the inhaler 100. Provide wirelessly to external devices including. Personalized data is provided to external devices in real time, thereby indicating the number of uses from the inhaler 100 and how the inhaler 100 was used, as well as the subject's lung function and / Or it is possible to predict exacerbation risk based on personalized data about the inhaler user, such as real-time data about treatment. The external device has software for processing the received information and providing compliance feedback to the user of the inhaler 100 via a graphic user interface (GUI).

Airflow metrics include average inhalation / discharge flow and / or maximum inhalation / discharge flow (eg, maximum inhalation flow received) and / or inhalation / discharge volume and / or time vs. inhalation / discharge maximum and / or inhalation. / Contains personalized data collected in real time from the inhaler 100, such as one or more of the duration of the discharge. Metrics for airflow also represent the direction of airflow through flow path 119. That is, a negative pressure change corresponds to inhalation from the mouthpiece 106, while a positive pressure change corresponds to exhalation to the mouthpiece 106. In calculating airflow metrics, the electronic module 120 is configured to eliminate or minimize distortions caused by environmental conditions. For example, the electronic module 120 makes calculations to take into account changes in atmospheric pressure before or after calculating the metrics for airflow. One or more pressure measurements and / or airflow metrics are recorded in the memory of the electronic module 120 after being time stamped.

In addition to the airflow metrics, the inhaler 100 or another calculator uses the airflow metrics to generate additional personalized data. For example, the controller of the electronic module 120 of the inhaler 100 is understood to have metrics about airflow as medical parameters such as, for example, maximum inspiratory flow and / or maximum expiratory flow and / or forced expiratory lung capacity (FEV1) per second. Translate into other metrics that represent the subject's lung function and / or lung health. The electronic module 120 of the inhaler 100 calculates a measure of a subject's lung function and / or lung health using a mathematical model such as a regression model. Mathematical models identify the correlation between total inhalation volume and FEV1. Mathematical models identify the correlation between maximum inspiratory flow rate and FEV1. Mathematical models identify the correlation between total inhalation volume and maximum expiratory flow. Mathematical models identify the correlation between maximum inspiratory and expiratory flow rates.

The battery 126 supplies power to the components of the PCB 122. The battery 126 comprises any suitable power source for supplying power to the electronic module 120, such as a coin battery. Battery 126 is rechargeable or non-rechargeable. The battery 126 is housed in the battery holder 124. The battery holder 124 is attached to the PCB 122 so that the battery 126 is in continuous contact with the PCB 122 and / or with electrical contact with parts of the PCB 122. The battery 126 has a specific battery capacity that affects the life of the battery 126. As further described below, the distribution of power from the battery 126 to one or more components of the PCB 122 is such that the battery 126 is electronic over the useful life of the inhaler 100 and / or the lifetime of the drug contained therein. It is managed to ensure that the module 120 is powered.

In the connected state, the communication circuit and the memory are turned on, and the electronic module 120 is paired with an external device such as a smartphone. The controller retrieves the data from the memory and wirelessly transmits the data to the external device. The controller searches for the latest stored data in memory and sends it. The controller also retrieves and sends some of the most recently stored data in memory. For example, the controller can determine which part of the data has already been transmitted to the external device and then transmit the part of the data that has not yet been transmitted. Alternatively, the external device requests specific data, such as data collected by the electronic module 120, from the controller after a specific time or after the latest transmission to the external device. The controller searches the memory for that particular data, if any, and sends the searched data to an external device.

The data stored in the memory of the electronic module 120 (eg, the signal generated by the switch and / or the pressure measurement obtained by the sensor system 128 and / or the metric about the airflow calculated by the controller of the PCB 122) is sent to the external device. Will be sent. The external device processes and analyzes the data and calculates useful parameters associated with the inhaler 100. Further, the mobile application on the mobile device generates feedback to the user based on the data received from the electronic module 120. For example, a mobile application may generate daily, monthly, and annual reports and / or confirm or notify users of error events and / or provide useful feedback to users and / or similar. I do.

The examples described above are merely examples for explaining the configuration of the present invention, and thus do not limit the configuration of the present invention. Another variation of the embodiments described above can be understood and provided by those skilled in the art from studying the drawings, specification and claims in practice of the present invention. The fact that certain means are set forth in different citation-type claims does not indicate that a combination of those means cannot be used in an advantageous manner.

Claims (23)

A system that calculates the probability of exacerbation of asthma in a subject.
A first inhaler for administering the first inhaler to the subject is provided, the first inhaler to measure the emergency inhalation performed by the subject using the first inhaler. Has a use detection system configured in, and also
A second inhaler, selective for administering maintenance medication to the subject during regular inhalation, and
A sensor system configured to measure airflow parameters during the emergency inhalation and / or the periodic inhalation using the second inhaler when the second inhaler is used in the system. When,
The number of such emergency inhalations is measured during the first period, and the measured values of the parameters are received during at least several times of the emergency and / or the periodic inhalations, and the weighted model is used to obtain the emergency inhalation. With a processor configured to calculate the probability of exacerbation of the asthma based on the number of times and the measured values of the parameters.
The model is characterized in that the number of emergency inhalations is weighted to be more significant than the measured value of the parameter in the calculation of the probability. The system according to claim 1, wherein the probability of exacerbation of asthma is the probability that the exacerbation of asthma occurs within the exacerbation period following the first period. The system according to claim 1 or 2, wherein the first period is 1 to 15 days. The system according to any one of claims 1 to 3, wherein the parameter is at least one of a maximum inhalation flow rate, an inhalation amount and an inhalation duration. The processor is further configured to determine the minimum maximum inhalation flow rate from the maximum inhalation flow rate measured during the emergency inhalation and / or the periodic inhalation within a second period of exacerbation of the asthma. The system of claim 4, wherein the probabilities are calculated in part based on the minimum maximum inhalation flow rate and optionally the second period is 1-5 days. 5. The processor of claim 5, wherein the processor is configured to calculate the probability of exacerbation of the asthma, in part, based on a change in the minimum maximum inhalation flow rate with respect to a reference maximum inhalation flow rate. system. The processor is further configured to determine the minimum inhalation amount from the inhalation amount measured during the emergency inhalation and / or regular inhalation performed within the third period, with a probability of exacerbation of the asthma. The system according to any one of claims 4 to 6, which is based in part on the minimum inhalation amount and optionally has the third period of 1 to 5 days. The system of claim 7, wherein the processor is configured to calculate the probability of exacerbation of the asthma, in part, based on a change in the minimum inhalation amount with respect to a reference inhalation amount. The processor is configured to determine the minimum inhalation duration from the inhalation duration measured during emergency inhalation and / or regular inhalation within a fourth period, and the probability of exacerbation of the asthma is partially said. The system according to any one of claims 4 to 8, which is calculated based on the minimum inhalation duration and optionally has the fourth period of 1 to 5 days. 9. The system of claim 9, wherein the processor is configured to partially calculate the probability of exacerbation of the asthma based on a change in the minimum inhalation duration with respect to a reference inhalation duration. The sensor system comprises a pressure sensor, optionally the use detection system has another pressure sensor, and the pressure sensor and the other pressure sensor have the same or different configurations from each other. The system according to any one of claims 1 to 10. The first inhaler is
With the drug tank,
It has a weighing assembly configured to weigh a single dose of the drug from the drug tank.
The use detection system is configured to record the metering of the single dose of the drug by the weighing assembly, whereby the single dose of the drug is performed by the use of the first inhaler of the subject. The system according to any one of claims 1 to 11, wherein the first-aid inhalation is shown. Further equipped with a user interface for receiving input of an index indicating the pathological condition of the respiratory disease suffered by the subject.
The processor is configured to calculate the probability of exacerbation of the asthma using the weighted model based on the number of emergency inhalations and the measured values of the parameters and the index received. The system according to any one of claims 1 to 12, wherein the system is characterized by the above. A method for calculating the probability of exacerbation of asthma in a subject.
Received the number of first-aid inhalations of the first-aid drug performed by the subject within the first period,
Receives measured values of airflow parameters during at least several such emergency inhalations and / or during routine inhalations of maintenance medication performed by the subject.
Using a weighted model, the probability of exacerbation of the asthma was determined based on the number of emergency inhalations and the measured values of the parameters.
The model is characterized in that the number of emergency inhalations is weighted to be more significant than the measured value of the parameter in the calculation of the probability. Further, a first inhaler for administering the first-aid drug to the subject is prepared, and the first inhaler is executed by the subject using the first inhaler. 14. The method of claim 14, wherein the method comprises a use detection system configured to measure emergency inhalation. 14 or 15. The method of claim 15, further comprising preparing a sensor system configured to measure the parameters with respect to airflow during the emergency inhalation and / or the periodic inhalation. A computer program comprising computer program code adapted to perform the method of claim 14, when executed on a computer. A method of treating an exacerbation of asthma in a subject
The method according to any one of claims 14 to 16 is carried out, and the method is carried out.
Determining whether the probability reaches or exceeds a predetermined upper limit, or determines whether the probability reaches a predetermined lower limit or is lower than the lower limit.
A method comprising treating asthma based on the probability of reaching or exceeding the upper limit, or based on the probability of reaching or lowering the lower limit. The treatment comprises switching from a first treatment plan to a second treatment plan for the subject based on the probability of reaching or exceeding the upper limit, wherein the second treatment plan is said. The method of claim 18, wherein the method is designed to address the exacerbation of the asthma at a higher risk than in the case of the first treatment plan. The second treatment regimen comprises administration of a biopharmacy, optionally said the biopharmacy comprises any one of omalizumab, mepolizumab, reslizumab, benralizumab and dupilumab or a combination of two or more thereof. 19. The method of claim 19. The treatment comprises switching from a first treatment plan to a third treatment plan for the subject based on the probability that the lower limit has been reached or is lower than the lower limit, wherein the third treatment plan is said. The method of claim 18, characterized in that it is made to address the exacerbation of the asthma at a lower risk than in the case of the first treatment plan. It ’s a way to diagnose the exacerbation of asthma.
The method according to any one of claims 14 to 16 is carried out, and the method is carried out.
Determining whether the probability reaches or exceeds a predetermined upper limit representing the exacerbation of the asthma.
A method comprising diagnosing an exacerbation of the asthma based on the probability of reaching or exceeding the upper limit. A method for demarcating subpopulations of subjects
The method according to any one of claims 14 to 16 is carried out for each of the subjects belonging to the population of the subjects.
Thereby, the probability for each of the subjects belonging to the population is calculated.
A threshold of probability or a range of probabilities that distinguishes the probabilities calculated for the subpopulation from the probabilities calculated for the rest of the population is set.
A method comprising defining the subpopulation from the rest of the population using the probability threshold or the range of probabilities.

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